I insert this line of code in 3.xxx version, but compile an error, How should I modify it in the code. Here is all my code. Please see all the big guys to help you solve it. Thanks very much

import os import mmcv import numpy as np import matplotlib.pyplot as plt import pandas as pd

from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from mmdet.apis import init_detector, inference_detector from mmengine.config import Config

from mmdet.datasets import build_dataset

from mmdet.registry import VISUALIZERS

def getPrecisions(config_file, result_file, metric="bbox"): """plot precison-recall curve based on testing results of pkl file.

    Args:
        config_file (list[list | tuple]): config file path.
        result_file (str): pkl file of testing results path.
        metric (str): Metrics to be evaluated. Options are
            'bbox', 'segm'.
"""

cfg = Config.fromfile(config_file)
print(cfg.test_dataloader.dataset)
# turn on test mode of dataset
# if isinstance(cfg.dataset.test, dict):
#     cfg.data.test.test_mode = True
# elif isinstance(cfg.dataset.test, list):
#     for ds_cfg in cfg.dataset.test:
#         ds_cfg.test_mode = True
if isinstance(cfg.test_dataloader.dataset, dict):
    cfg.test_dataloader.dataset.test_mode = True
elif isinstance(cfg.test_dataloader.dataset, list):
    for ds_cfg in cfg.test_dataloader.dataset:
        ds_cfg.test_mode = True

# build dataset
**dataset = build_dataset(cfg.test_dataloader.dataset)**
# load result file in pkl format
pkl_results = mmcv.load(result_file)
# convert pkl file (list[list | tuple | ndarray]) to json
json_results, _ = dataset.format_results(pkl_results)
# initialize COCO instance
coco = COCO(annotation_file=cfg.data.test.ann_file)
coco_gt = coco
coco_dt = coco_gt.loadRes(json_results[metric])
# initialize COCOeval instance
coco_eval = COCOeval(coco_gt, coco_dt, metric)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
'''
precisions[T, R, K, A, M]
T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9
R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100
K: category, idx from 0 to ...
A: area range, (all, small, medium, large), idx from 0 to 3
M: max dets, (1, 10, 100), idx from 0 to 2
'''
return coco_eval.eval["precision"]

def PR(config, result, out, thr=0.5): """Export PR Excel data

    Args:
        config_file (list[list | tuple]): config file path.
        result_file (str): pkl file of testing results path.
        out (str): path of excel file
        thr(float): output PR Threshold. Optional range: {-1, [0.5, 0.95]}
            If thr == -1: Threshold is 0.5-0.95
"""

precisions = getPrecisions(config, result)

recall = np.mat(np.arange(0.0, 1.01, 0.01)).T

if thr == -1:
    mAP_all_pr = np.mean(precisions[:, :, :, 0, 2], axis=0)
else:
    T = int((thr - 0.5) / 0.05)
    mAP_all_pr = precisions[T, :, :, 0, 2]

data = np.hstack((np.hstack((recall, mAP_all_pr[:, 1:])), np.mat(np.mean(mAP_all_pr[:, 1:], axis=1)).T))

df = pd.DataFrame(data)

df.to_excel(out, index=False)

config='C:/Users/98485/Desktop/mmdetection-main/configs/retinanet/retinanet_r50_fpn_2x_coco_vitV2VPT.py' result_file='our.pkl' PR(config,result_file,'/result_excel')

open-mmlab / mmdetection

How to modify this line of code?from mmdet.datasets import build_dataset #11595

from mmdet.datasets import build_dataset

from mmdet.registry import VISUALIZERS